In the up-link (UL) of a wireless communication system, a number of mobile terminals (MT) may be active (i.e. transmitting) at the same time. As an example, a HSPA (High Speed Packet Access) cell may support approximately 200 simultaneous VoIP (Voice over Internet Protocol) users. When transmissions from these terminals arrive at the base station(s), the received signals often interfere with each other. Due to the interference between signals from different terminals, it may be hard (or even impossible) to separate the signals belonging to different terminals at a single base station. A further related obstacle is that the strength of a signal from a particular terminal may be weak and/or vary rapidly over time.
One possible way to, at least to some extent, mitigate these problems is to employ multiple receive antennas at the base station. Multi-antenna receivers make possible the use of interference suppression and/or interference cancellation (IC). Multi-antenna receivers also provide diversity gain that may be used to mitigate fading. Multi-antenna solutions for wireless communication system traditionally utilize two or more antennas situated at the same base station site, thus limiting the geographical separation of the antennas.
A further extension of the principles used in multi-antenna solutions is to collect UL received signals from two or more base stations. One advantage with such an approach is that a larger geographical separation of the points of reception is achieved. A collection of signals in this context may typically involve collecting the base band signals from different base stations and use them for coordinated signal processing in a processing unit common to the base stations involved. An example application of this approach is the Coordinated Multi-Point (CoMP) evaluated for the Universal Mobile Telecommunication Standard Long Term Evolution (UMTS LTE).
When such an approach is used it will be possible to receive signals from different terminals and separate the respective signals from each other with improved average confidence. This is, at least partly, due to that interference suppression, interference cancellation, and/or other processing algorithms used may utilize additional joint information that becomes available about the signals sent from the different terminals. For example, more degrees of freedom are available for interference suppression and/or interference cancellation algorithms. Furthermore, robustness against fading may be further improved by utilizing that several versions (typically having different signal path combinations and thereby different fading patterns) of the same transmitted signal are available.
Utilizing signals from several base stations in coordinated processing (e.g. CoMP), the advanced interference cancellation/suppression algorithms and/or other coordinated signal processing may require significant signal processing resources. This is particularly true if the processing involves separation of signals from a large number of terminals, where at least some of the individual signals from different terminals are strongly coupled due to that their propagation channels have similar characteristics from the receiving base stations point of view. Signals that are strongly coupled in this way are typically harder to separate by signal processing.
The terminals may be effectively separated in the coordinated signal processing by use of non-linear receiver structures (e.g. receivers based on SIC—successive interference cancellation—or PIC—parallel interference cancellation). Alternatively, linear interference suppression methods may be applied (e.g. modified RAKE-based receivers such as GRAKEX+, where X refers to the number of RX—receiver—antennas per site). Using linear algorithms typically does not yield as efficient interference suppression as does non-linear algorithms. However, linear algorithms are typically less complex. Furthermore, it may be beneficial to employ linear algorithms since they may already be implementable in existing hardware in e.g. the base stations, while employing non-linear algorithms may require a hardware update.
Traditional strategies for scheduling of UL transmissions from the terminals focus on minimizing single-user SIR (signal-to-interference ratio). Thus, potential interference from other terminals is not accounted for which leads to notably degraded performance when there are multiple terminals, due to a large residual inter-terminal interference at the base stations and/or at the unit for coordinated processing (e.g. CoMP). This performance loss is particularly noticeable when linear interference suppression receivers are employed, because their interference suppression capability is worse than that of non-linear algorithm receivers.
Thus, alternative scheduling approaches have been developed to improve the terminal separation and thus the overall performance.
For example, the signals received from multiple terminals may be treated as MU-MIMO (multi-user multiple input multiple output) signals at the base stations and/or at the unit for coordinated processing (e.g. CoMP), whereby existing MU-MIMO scheduling approaches may be applied with the aim to maximize the spatial orthogonality between the simultaneously transmitting terminals.
In another example scheduling approach, several hypotheses may be evaluated where the terminals are divided into a number of groups in different ways for each hypothesis. The terminals in a group are intended for simultaneous transmission. The evaluation may comprise calculation of SIR or achievable data rate for each terminal and hypothesis, and the hypothesis having groups with maximum accumulated SIR or maximum accumulated rate may be used for scheduling.
Scheduling approaches that are MU-MIMO oriented are relatively simple. However, the number of simultaneously transmitting terminals is limited in that it cannot exceed the number of RX antennas at the base station (or the total number of RX antennas in a coordinated processing (e.g. CoMP) cell). Such a limitation makes the approach unsuitable for use when a coordinated processing cell should be designed to handle tens (or hundreds) of active terminals.
In principle, the hypothesis-based methods enable handling of an unlimited number of terminals. However, the associated computational complexity grows rapidly as the number of terminals increases. Consider, for example, a moderate sized set of N=60 terminals and suppose the terminals should be divided into K=3 groups of L=20 terminals in each. A full-complexity search for this realistic example would need to evaluate SIR or data rate for all possible groups of 20 terminals (60!/(20!40!)≈4.2·1015 groups) and choose the 3 disjoint groups that have the best performance according to the evaluation. This is clearly un-preferable and possibly infeasible.
Greedy algorithms, that do not evaluate all hypotheses, may be applied to mitigate the extreme complexity growth. In such algorithms groups may be successively grown by one terminal at a time. The terminals are processed one by one, evaluating the hypotheses of adding the particular terminal to each of the groups, and the terminal is finally added to the group where the resulting SIR or achievable data rate is optimized given the hypotheses for that particular terminal. Thus, the corresponding accumulated performance measure is incremented each time a terminal is added. Such an approach is less complex than an exhaustive evaluation, but yet it also suffers from large complexity. Furthermore, all terminals must be accommodated in a group and the possibility, that any of the terminals processed last (or near the end of the algorithm) destroys the performance regardless of which group it is added to, is not negligible.
Additional simplifications are possible to further reduce the number of hypotheses. However, even with a quite small number of hypotheses to evaluate each hypothesis requires building a hypothesis-specific interference model and computing a corresponding performance measure. These are operations requiring significant computational resources.
Therefore, there is a need for a scheduling approach for effectively scheduling terminals for UL transmission by dividing the terminals into a number of groups. Preferably, the approach should be a low-complexity algorithm able to handle an arbitrary number of terminals and achieving a close to optimal result (e.g. in terms of inter-terminal interference in each group).